As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a "naive space", which does not take into account the protocol used, can often lead to counterintuitive results. Here we examine why. A criterion known as CAR ("coarsening at random") in the statistical literature characterizes when "naive" conditioning in a naive space works. We show that the CAR condition holds rather infrequently, and we provide a procedural characterization of it, by giving a randomized algorithm that generates all and only distributions for which CAR holds. This substantially extends previous characterizations of CAR. We also consider more generalized notions of update such as Jeffrey conditioning and minimizing relative entropy (MRE). We give a generalization of the CAR condition that characterizes when Jeffrey conditioning leads to appropriate answers, and show that there exist some very simple settings in which MRE essentially never gives the right results. This generalizes and interconnects previous results obtained in the literature on CAR and MRE.

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I discuss the role of learning in economic policy choices. I test whether choices of policies are driven by experience under them. A Bayesian approach is adopted to tackle this issue and hence, the paper is also an account of the possibilities and limits of this approach. The decision to sign IMF agreements is used as illustration. The model of rational learning suggests that governments are more likely to enter into agreements with the IMF after learning from their own and from the world experience that average results under agreements are better. Also, governments are prone to take risks when they observe very good performers under IMF agreements at the world and at the regional level. However, they are unlikely to continue under a policy that is costly as soon as growth resumes in the region. The rationalistic reading of this behavior is that governments avoid remaining under a costly policy in isolation. A more detailed research is needed in order to adjudicate among this account of switches and an alternative one that emphasizes deviations to the rational rule as the ultimate cause of choices.

Formal epistemology is a discipline with various branches and sub-branches dealing with precise representations of attitudes and their flux. The formal tools utilized for this purpose go from standard probability theory to non-standard (or infinitesimal) probability, to decision theoretical tools to logical tools.